Robust Vegetation Detection Using RGB Colour Composites and Isoclust Classification of the Landsat TM Image

Lemenkova, P.

Автор: Lemenkova Polina
Журнал: Geomatics, Landmanagement and Landscape
Том: 4
Год издания: 2021
Издательство: University of Agriculture in Krakow
Местоположение издательства: Poland
Первая страница: 147
Последняя страница: 167
DOI: 10.15576/GLL/2021.4.147
Аннотация: The paper presents the application of ArcGIS for environmental modelling of the landscapes in northern Iceland (17.00°W–23.00°W, 64.30°N–67.00°N). The aim was to explore the vegetation distribution by NDVI and ISOCLUST classification of the land cover types. Data include the Landsat TM image. Freely available satellite remote sensing data from the Landsat mission have been processed by GIS to deliver information on land cover types from image classification and NDVI vegetation index. Landsat products provide geospatial data on regional scale with moderate temporal (weekly) and spatial (30–10 m) resolution, making them useful for environmental monitoring and landscape studies. The tools include the ArcGIS software used for raster processing. Data processing was performed in the three steps: 1) comparative analysis of the visualized sixteen band combinations to assess the distinguishability of vegetation and other land cover types in colour composites; 2) computed NDVI standardized vegetation index; 3) unsupervised classification of the Landsat TM by the ISOCLUST algorithm. Large glaciers Hofsjökull and Langjökull were detected on various colour composites, and the visibility of the water/land borders is assessed (Blöndulón lake), agricultural areas near the Varmahlíð, vegetated areas around the Akrahreppur municipality. Computing the NDVI and using ISOCLUST by ArcGIS software enabled to distinguish various land cover types and map landscapes in the study area. The computed NDVI shown the presence and condition of vegetation, that is, a rela- tive biomass in the area of northern Iceland. The NDVI was used based on the contrast of the two channels from a multispectral Landsat TM raster data.
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Robust Vegetation Detection Using RGB Colour Composites and Isoclust Classification of the Landsat TM Imageстатья

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